Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/70050
標題: Recurrent neuro-fuzzy modeling and fuzzy MDPP control for flexible servomechanisms
作者: Lin, C.S.
Yang, T.C.
Jou, Y.C.
Lin, L.C.
關鍵字: recurrent neuro-fuzzy model;TS fuzzy model;RLS algorithm;fuzzy MDPP;control;servomechanism;flexibility;friction;genetic algorithms;systems;friction;networks
Project: Journal of Intelligent & Robotic Systems
期刊/報告no:: Journal of Intelligent & Robotic Systems, Volume 38, Issue 2, Page(s) 213-235.
摘要: 
This paper considers the nonlinear system identification and control for flexible servomechanisms. A multi-step-ahead recurrent neuro-fuzzy model consisting of local linear ARMA (autoregressive moving average) models with bias terms is suggested for approximating the dynamic behavior of a servomechanism including the effects of flexibility and friction. The RLS ( recursive least squares) algorithm is adopted for obtaining the optimal consequent parameters of the rules. Within each fuzzy operating region, a local MDPP ( minimum degree pole placement) control law with integral action can be constructed based on the estimated local model. Then a fuzzy controller composed of these local MDPP controls can be easily constructed for the servomechanism. The techniques are illustrated using computer simulations.
URI: http://hdl.handle.net/11455/70050
ISSN: 0921-0296
DOI: 10.1023/a:1027339220324
Appears in Collections:期刊論文

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